Overview

Brought to you by YData

Dataset statistics

Number of variables13
Number of observations52416
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory4.6 MiB
Average record size in memory92.0 B

Variable types

Numeric12
Categorical1

Alerts

Year has constant value "2017" Constant
DiffuseFlows is highly overall correlated with GeneralDiffuseFlowsHigh correlation
GeneralDiffuseFlows is highly overall correlated with DiffuseFlowsHigh correlation
Hour is highly overall correlated with PowerConsumption_Zone1 and 1 other fieldsHigh correlation
PowerConsumption_Zone1 is highly overall correlated with Hour and 2 other fieldsHigh correlation
PowerConsumption_Zone2 is highly overall correlated with Hour and 2 other fieldsHigh correlation
PowerConsumption_Zone3 is highly overall correlated with PowerConsumption_Zone1 and 1 other fieldsHigh correlation
Hour has 2184 (4.2%) zeros Zeros
Minute has 8736 (16.7%) zeros Zeros

Reproduction

Analysis started2025-01-19 05:09:03.715467
Analysis finished2025-01-19 05:09:43.779854
Duration40.06 seconds
Software versionydata-profiling vv4.12.1
Download configurationconfig.json

Variables

Temperature
Real number (ℝ)

Distinct3437
Distinct (%)6.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean18.810024
Minimum3.247
Maximum40.01
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size819.0 KiB
2025-01-19T05:09:44.004797image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Quantile statistics

Minimum3.247
5-th percentile9.8
Q114.41
median18.78
Q322.89
95-th percentile28.39
Maximum40.01
Range36.763
Interquartile range (IQR)8.48

Descriptive statistics

Standard deviation5.8154758
Coefficient of variation (CV)0.30916898
Kurtosis-0.30332122
Mean18.810024
Median Absolute Deviation (MAD)4.26
Skewness0.19671914
Sum985946.22
Variance33.819759
MonotonicityNot monotonic
2025-01-19T05:09:44.294370image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
15.18 58
 
0.1%
20.76 56
 
0.1%
19.79 55
 
0.1%
20.74 52
 
0.1%
20.83 51
 
0.1%
15.85 51
 
0.1%
15.84 51
 
0.1%
21 50
 
0.1%
20.89 50
 
0.1%
20.37 49
 
0.1%
Other values (3427) 51893
99.0%
ValueCountFrequency (%)
3.247 1
< 0.1%
3.441 1
< 0.1%
3.541 1
< 0.1%
3.555 1
< 0.1%
3.582 1
< 0.1%
3.629 1
< 0.1%
3.638 1
< 0.1%
3.662 1
< 0.1%
3.681 1
< 0.1%
3.706 1
< 0.1%
ValueCountFrequency (%)
40.01 1
< 0.1%
39.78 1
< 0.1%
39.76 1
< 0.1%
39.74 1
< 0.1%
39.73 1
< 0.1%
39.7 1
< 0.1%
39.67 1
< 0.1%
39.6 1
< 0.1%
39.59 1
< 0.1%
39.55 1
< 0.1%

Humidity
Real number (ℝ)

Distinct4443
Distinct (%)8.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean68.259518
Minimum11.34
Maximum94.8
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size819.0 KiB
2025-01-19T05:09:44.635586image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Quantile statistics

Minimum11.34
5-th percentile39.45
Q158.31
median69.86
Q381.4
95-th percentile88.9
Maximum94.8
Range83.46
Interquartile range (IQR)23.09

Descriptive statistics

Standard deviation15.551177
Coefficient of variation (CV)0.2278243
Kurtosis-0.12185965
Mean68.259518
Median Absolute Deviation (MAD)11.54
Skewness-0.62516601
Sum3577890.9
Variance241.83911
MonotonicityNot monotonic
2025-01-19T05:09:44.902143image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
85.9 197
 
0.4%
84.6 190
 
0.4%
85 189
 
0.4%
86.6 187
 
0.4%
86.3 186
 
0.4%
85.8 185
 
0.4%
87.2 175
 
0.3%
86.8 173
 
0.3%
87.4 171
 
0.3%
86.9 171
 
0.3%
Other values (4433) 50592
96.5%
ValueCountFrequency (%)
11.34 2
< 0.1%
11.57 1
< 0.1%
11.94 1
< 0.1%
12.27 1
< 0.1%
12.3 1
< 0.1%
12.6 1
< 0.1%
12.74 1
< 0.1%
12.87 1
< 0.1%
13.04 1
< 0.1%
13.07 1
< 0.1%
ValueCountFrequency (%)
94.8 3
 
< 0.1%
94.7 4
 
< 0.1%
94.6 1
 
< 0.1%
94.5 2
 
< 0.1%
94.4 1
 
< 0.1%
94.3 2
 
< 0.1%
94.2 4
 
< 0.1%
94.1 6
< 0.1%
94 9
< 0.1%
93.9 10
< 0.1%

WindSpeed
Real number (ℝ)

Distinct548
Distinct (%)1.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1.9594889
Minimum0.05
Maximum6.483
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size819.0 KiB
2025-01-19T05:09:45.185505image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Quantile statistics

Minimum0.05
5-th percentile0.069
Q10.078
median0.086
Q34.915
95-th percentile4.923
Maximum6.483
Range6.433
Interquartile range (IQR)4.837

Descriptive statistics

Standard deviation2.348862
Coefficient of variation (CV)1.1987116
Kurtosis-1.7831692
Mean1.9594889
Median Absolute Deviation (MAD)0.016
Skewness0.46242332
Sum102708.57
Variance5.5171525
MonotonicityNot monotonic
2025-01-19T05:09:45.469907image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.082 2291
 
4.4%
0.083 1979
 
3.8%
0.084 1831
 
3.5%
0.081 1804
 
3.4%
0.085 1513
 
2.9%
0.08 1502
 
2.9%
0.073 1485
 
2.8%
4.919 1430
 
2.7%
4.916 1375
 
2.6%
0.072 1369
 
2.6%
Other values (538) 35837
68.4%
ValueCountFrequency (%)
0.05 1
 
< 0.1%
0.053 5
 
< 0.1%
0.054 10
< 0.1%
0.055 13
< 0.1%
0.056 9
< 0.1%
0.057 17
< 0.1%
0.058 4
 
< 0.1%
0.059 7
< 0.1%
0.06 9
< 0.1%
0.061 11
< 0.1%
ValueCountFrequency (%)
6.483 1
< 0.1%
6.325 1
< 0.1%
6.2 1
< 0.1%
5.817 1
< 0.1%
5.69 1
< 0.1%
5.402 1
< 0.1%
5.375 1
< 0.1%
5.044 1
< 0.1%
5.019 1
< 0.1%
5.014 2
< 0.1%

GeneralDiffuseFlows
Real number (ℝ)

High correlation 

Distinct10504
Distinct (%)20.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean182.69661
Minimum0.004
Maximum1163
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size819.0 KiB
2025-01-19T05:09:45.748009image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Quantile statistics

Minimum0.004
5-th percentile0.037
Q10.062
median5.0355
Q3319.6
95-th percentile782
Maximum1163
Range1162.996
Interquartile range (IQR)319.538

Descriptive statistics

Standard deviation264.40096
Coefficient of variation (CV)1.4472132
Kurtosis0.40276752
Mean182.69661
Median Absolute Deviation (MAD)5.0095
Skewness1.3069729
Sum9576225.7
Variance69907.867
MonotonicityNot monotonic
2025-01-19T05:09:46.033626image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.055 1576
 
3.0%
0.062 1557
 
3.0%
0.051 1497
 
2.9%
0.059 1474
 
2.8%
0.066 1459
 
2.8%
0.048 1388
 
2.6%
0.044 1292
 
2.5%
0.073 1262
 
2.4%
0.04 1125
 
2.1%
0.077 1116
 
2.1%
Other values (10494) 38670
73.8%
ValueCountFrequency (%)
0.004 3
 
< 0.1%
0.007 9
 
< 0.1%
0.011 38
 
0.1%
0.015 97
 
0.2%
0.018 184
 
0.4%
0.022 309
 
0.6%
0.026 436
0.8%
0.029 566
1.1%
0.033 699
1.3%
0.037 924
1.8%
ValueCountFrequency (%)
1163 1
< 0.1%
1122 1
< 0.1%
1102 1
< 0.1%
1099 1
< 0.1%
1082 1
< 0.1%
1069 1
< 0.1%
1055 1
< 0.1%
1051 1
< 0.1%
1050 1
< 0.1%
1044 1
< 0.1%

DiffuseFlows
Real number (ℝ)

High correlation 

Distinct10449
Distinct (%)19.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean75.028022
Minimum0.011
Maximum936
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size819.0 KiB
2025-01-19T05:09:46.322068image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Quantile statistics

Minimum0.011
5-th percentile0.085
Q10.122
median4.456
Q3101
95-th percentile331.85
Maximum936
Range935.989
Interquartile range (IQR)100.878

Descriptive statistics

Standard deviation124.21095
Coefficient of variation (CV)1.6555274
Kurtosis7.0029015
Mean75.028022
Median Absolute Deviation (MAD)4.393
Skewness2.4569065
Sum3932668.8
Variance15428.36
MonotonicityNot monotonic
2025-01-19T05:09:46.590846image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.115 1260
 
2.4%
0.122 1218
 
2.3%
0.119 1201
 
2.3%
0.126 1150
 
2.2%
0.111 1140
 
2.2%
0.13 1093
 
2.1%
0.104 1085
 
2.1%
0.1 963
 
1.8%
0.137 926
 
1.8%
0.096 915
 
1.7%
Other values (10439) 41465
79.1%
ValueCountFrequency (%)
0.011 1
 
< 0.1%
0.019 3
 
< 0.1%
0.022 2
 
< 0.1%
0.026 4
 
< 0.1%
0.03 10
 
< 0.1%
0.033 18
 
< 0.1%
0.037 20
 
< 0.1%
0.041 34
0.1%
0.044 53
0.1%
0.048 74
0.1%
ValueCountFrequency (%)
936 1
< 0.1%
933 1
< 0.1%
922 2
< 0.1%
909 1
< 0.1%
903 1
< 0.1%
897 1
< 0.1%
863 1
< 0.1%
856 1
< 0.1%
855 1
< 0.1%
851 1
< 0.1%

PowerConsumption_Zone1
Real number (ℝ)

High correlation 

Distinct27709
Distinct (%)52.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean32344.971
Minimum13895.696
Maximum52204.395
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size819.0 KiB
2025-01-19T05:09:46.852384image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Quantile statistics

Minimum13895.696
5-th percentile21867.342
Q126310.669
median32265.92
Q337309.018
95-th percentile44712.058
Maximum52204.395
Range38308.699
Interquartile range (IQR)10998.349

Descriptive statistics

Standard deviation7130.5626
Coefficient of variation (CV)0.22045352
Kurtosis-0.75405439
Mean32344.971
Median Absolute Deviation (MAD)5513.8039
Skewness0.22886369
Sum1.695394 × 109
Variance50844922
MonotonicityNot monotonic
2025-01-19T05:09:47.678250image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
34560 30
 
0.1%
23040 24
 
< 0.1%
28800 19
 
< 0.1%
25920 18
 
< 0.1%
23672.42196 13
 
< 0.1%
35441.31148 13
 
< 0.1%
21063.34426 12
 
< 0.1%
21950.76923 12
 
< 0.1%
31680 12
 
< 0.1%
22880.68085 11
 
< 0.1%
Other values (27699) 52252
99.7%
ValueCountFrequency (%)
13895.6962 1
< 0.1%
13920 1
< 0.1%
13932.1519 1
< 0.1%
14090.12658 1
< 0.1%
14327.08861 1
< 0.1%
14557.97468 1
< 0.1%
14612.65823 1
< 0.1%
15013.67089 1
< 0.1%
15524.05063 1
< 0.1%
15572.65823 1
< 0.1%
ValueCountFrequency (%)
52204.39512 1
< 0.1%
52146.85905 1
< 0.1%
52038.1798 1
< 0.1%
51955.07214 1
< 0.1%
51916.71476 1
< 0.1%
51820.82131 1
< 0.1%
51776.07103 1
< 0.1%
51737.71365 1
< 0.1%
51731.32075 1
< 0.1%
51718.53496 1
< 0.1%

PowerConsumption_Zone2
Real number (ℝ)

High correlation 

Distinct29621
Distinct (%)56.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean21042.509
Minimum8560.0815
Maximum37408.861
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size819.0 KiB
2025-01-19T05:09:47.951314image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Quantile statistics

Minimum8560.0815
5-th percentile13284.146
Q116980.766
median20823.168
Q324713.718
95-th percentile30387.137
Maximum37408.861
Range28848.779
Interquartile range (IQR)7732.9515

Descriptive statistics

Standard deviation5201.4659
Coefficient of variation (CV)0.24718848
Kurtosis-0.43739724
Mean21042.509
Median Absolute Deviation (MAD)3867.0469
Skewness0.32887602
Sum1.1029642 × 109
Variance27055247
MonotonicityNot monotonic
2025-01-19T05:09:48.237837image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
21600 16
 
< 0.1%
25200 12
 
< 0.1%
14148.32827 11
 
< 0.1%
23400 11
 
< 0.1%
22800 11
 
< 0.1%
16158.83576 11
 
< 0.1%
13732.5228 10
 
< 0.1%
18000 10
 
< 0.1%
13539.20973 10
 
< 0.1%
22962.16216 10
 
< 0.1%
Other values (29611) 52304
99.8%
ValueCountFrequency (%)
8560.081466 1
< 0.1%
8585.743381 1
< 0.1%
8633.401222 1
< 0.1%
8651.731161 1
< 0.1%
8787.372709 1
< 0.1%
8897.352342 1
< 0.1%
8912.016293 1
< 0.1%
9131.97556 1
< 0.1%
9307.942974 1
< 0.1%
9365.944272 1
< 0.1%
ValueCountFrequency (%)
37408.86076 1
< 0.1%
36645.56962 1
< 0.1%
36482.78775 1
< 0.1%
36437.17001 1
< 0.1%
36429.56705 1
< 0.1%
36391.55227 1
< 0.1%
36353.53749 1
< 0.1%
36201.47835 1
< 0.1%
36129.25026 1
< 0.1%
36110.24287 1
< 0.1%

PowerConsumption_Zone3
Real number (ℝ)

High correlation 

Distinct22838
Distinct (%)43.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean17835.406
Minimum5935.1741
Maximum47598.326
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size819.0 KiB
2025-01-19T05:09:48.508994image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Quantile statistics

Minimum5935.1741
5-th percentile9519.3277
Q113129.327
median16415.117
Q321624.1
95-th percentile29905.709
Maximum47598.326
Range41663.152
Interquartile range (IQR)8494.7738

Descriptive statistics

Standard deviation6622.1651
Coefficient of variation (CV)0.3712932
Kurtosis1.0863933
Mean17835.406
Median Absolute Deviation (MAD)3866.1593
Skewness1.0238715
Sum9.3486065 × 108
Variance43853071
MonotonicityNot monotonic
2025-01-19T05:09:48.763630image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
17280 26
 
< 0.1%
11520 19
 
< 0.1%
9600 17
 
< 0.1%
9450.180072 17
 
< 0.1%
9588.47539 16
 
< 0.1%
9173.589436 15
 
< 0.1%
9795.918367 15
 
< 0.1%
16366.26506 15
 
< 0.1%
9617.286915 15
 
< 0.1%
9329.171669 14
 
< 0.1%
Other values (22828) 52247
99.7%
ValueCountFrequency (%)
5935.17407 1
< 0.1%
6044.657863 1
< 0.1%
6061.944778 1
< 0.1%
6108.043217 1
< 0.1%
6119.567827 1
< 0.1%
6182.953181 1
< 0.1%
6200.240096 1
< 0.1%
6211.764706 1
< 0.1%
6223.289316 1
< 0.1%
6252.10084 1
< 0.1%
ValueCountFrequency (%)
47598.32636 2
< 0.1%
47580.25105 1
< 0.1%
47507.94979 1
< 0.1%
47441.67364 1
< 0.1%
47435.64854 1
< 0.1%
47429.62343 1
< 0.1%
47405.52301 1
< 0.1%
47291.04603 1
< 0.1%
47278.99582 1
< 0.1%
47266.94561 1
< 0.1%

Year
Categorical

Constant 

Distinct1
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size3.4 MiB
2017
52416 

Length

Max length4
Median length4
Mean length4
Min length4

Characters and Unicode

Total characters209664
Distinct characters4
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row2017
2nd row2017
3rd row2017
4th row2017
5th row2017

Common Values

ValueCountFrequency (%)
2017 52416
100.0%

Length

2025-01-19T05:09:48.999090image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-01-19T05:09:49.188348image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
ValueCountFrequency (%)
2017 52416
100.0%

Most occurring characters

ValueCountFrequency (%)
2 52416
25.0%
0 52416
25.0%
1 52416
25.0%
7 52416
25.0%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 209664
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
2 52416
25.0%
0 52416
25.0%
1 52416
25.0%
7 52416
25.0%

Most occurring scripts

ValueCountFrequency (%)
Common 209664
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
2 52416
25.0%
0 52416
25.0%
1 52416
25.0%
7 52416
25.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 209664
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
2 52416
25.0%
0 52416
25.0%
1 52416
25.0%
7 52416
25.0%

Month
Real number (ℝ)

Distinct12
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean6.510989
Minimum1
Maximum12
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size614.2 KiB
2025-01-19T05:09:49.361274image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q14
median7
Q39.25
95-th percentile12
Maximum12
Range11
Interquartile range (IQR)5.25

Descriptive statistics

Standard deviation3.440642
Coefficient of variation (CV)0.52843615
Kurtosis-1.2048069
Mean6.510989
Median Absolute Deviation (MAD)3
Skewness-0.0085027926
Sum341280
Variance11.838017
MonotonicityIncreasing
2025-01-19T05:09:49.572948image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
Histogram with fixed size bins (bins=12)
ValueCountFrequency (%)
1 4464
8.5%
3 4464
8.5%
5 4464
8.5%
7 4464
8.5%
8 4464
8.5%
10 4464
8.5%
4 4320
8.2%
6 4320
8.2%
9 4320
8.2%
11 4320
8.2%
Other values (2) 8352
15.9%
ValueCountFrequency (%)
1 4464
8.5%
2 4032
7.7%
3 4464
8.5%
4 4320
8.2%
5 4464
8.5%
6 4320
8.2%
7 4464
8.5%
8 4464
8.5%
9 4320
8.2%
10 4464
8.5%
ValueCountFrequency (%)
12 4320
8.2%
11 4320
8.2%
10 4464
8.5%
9 4320
8.2%
8 4464
8.5%
7 4464
8.5%
6 4320
8.2%
5 4464
8.5%
4 4320
8.2%
3 4464
8.5%

Day
Real number (ℝ)

Distinct31
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean15.678571
Minimum1
Maximum31
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size614.2 KiB
2025-01-19T05:09:49.788332image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile2
Q18
median16
Q323
95-th percentile29
Maximum31
Range30
Interquartile range (IQR)15

Descriptive statistics

Standard deviation8.7718216
Coefficient of variation (CV)0.55947837
Kurtosis-1.193143
Mean15.678571
Median Absolute Deviation (MAD)8
Skewness0.0074418652
Sum821808
Variance76.944855
MonotonicityNot monotonic
2025-01-19T05:09:50.044091image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
Histogram with fixed size bins (bins=31)
ValueCountFrequency (%)
1 1728
 
3.3%
2 1728
 
3.3%
28 1728
 
3.3%
27 1728
 
3.3%
26 1728
 
3.3%
25 1728
 
3.3%
24 1728
 
3.3%
23 1728
 
3.3%
22 1728
 
3.3%
21 1728
 
3.3%
Other values (21) 35136
67.0%
ValueCountFrequency (%)
1 1728
3.3%
2 1728
3.3%
3 1728
3.3%
4 1728
3.3%
5 1728
3.3%
6 1728
3.3%
7 1728
3.3%
8 1728
3.3%
9 1728
3.3%
10 1728
3.3%
ValueCountFrequency (%)
31 864
1.6%
30 1584
3.0%
29 1584
3.0%
28 1728
3.3%
27 1728
3.3%
26 1728
3.3%
25 1728
3.3%
24 1728
3.3%
23 1728
3.3%
22 1728
3.3%

Hour
Real number (ℝ)

High correlation  Zeros 

Distinct24
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean11.5
Minimum0
Maximum23
Zeros2184
Zeros (%)4.2%
Negative0
Negative (%)0.0%
Memory size614.2 KiB
2025-01-19T05:09:50.299698image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile1
Q15.75
median11.5
Q317.25
95-th percentile22
Maximum23
Range23
Interquartile range (IQR)11.5

Descriptive statistics

Standard deviation6.9222526
Coefficient of variation (CV)0.60193501
Kurtosis-1.2041743
Mean11.5
Median Absolute Deviation (MAD)6
Skewness0
Sum602784
Variance47.917581
MonotonicityNot monotonic
2025-01-19T05:09:50.521944image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
Histogram with fixed size bins (bins=24)
ValueCountFrequency (%)
0 2184
 
4.2%
1 2184
 
4.2%
22 2184
 
4.2%
21 2184
 
4.2%
20 2184
 
4.2%
19 2184
 
4.2%
18 2184
 
4.2%
17 2184
 
4.2%
16 2184
 
4.2%
15 2184
 
4.2%
Other values (14) 30576
58.3%
ValueCountFrequency (%)
0 2184
4.2%
1 2184
4.2%
2 2184
4.2%
3 2184
4.2%
4 2184
4.2%
5 2184
4.2%
6 2184
4.2%
7 2184
4.2%
8 2184
4.2%
9 2184
4.2%
ValueCountFrequency (%)
23 2184
4.2%
22 2184
4.2%
21 2184
4.2%
20 2184
4.2%
19 2184
4.2%
18 2184
4.2%
17 2184
4.2%
16 2184
4.2%
15 2184
4.2%
14 2184
4.2%

Minute
Real number (ℝ)

Zeros 

Distinct6
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean25
Minimum0
Maximum50
Zeros8736
Zeros (%)16.7%
Negative0
Negative (%)0.0%
Memory size614.2 KiB
2025-01-19T05:09:50.717299image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q110
median25
Q340
95-th percentile50
Maximum50
Range50
Interquartile range (IQR)30

Descriptive statistics

Standard deviation17.078414
Coefficient of variation (CV)0.68313657
Kurtosis-1.268578
Mean25
Median Absolute Deviation (MAD)15
Skewness0
Sum1310400
Variance291.67223
MonotonicityNot monotonic
2025-01-19T05:09:50.921294image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
Histogram with fixed size bins (bins=6)
ValueCountFrequency (%)
0 8736
16.7%
10 8736
16.7%
20 8736
16.7%
30 8736
16.7%
40 8736
16.7%
50 8736
16.7%
ValueCountFrequency (%)
0 8736
16.7%
10 8736
16.7%
20 8736
16.7%
30 8736
16.7%
40 8736
16.7%
50 8736
16.7%
ValueCountFrequency (%)
50 8736
16.7%
40 8736
16.7%
30 8736
16.7%
20 8736
16.7%
10 8736
16.7%
0 8736
16.7%

Interactions

2025-01-19T05:09:38.712240image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-01-19T05:09:05.211997image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-01-19T05:09:07.878393image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-01-19T05:09:11.610569image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-01-19T05:09:15.107282image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-01-19T05:09:17.736220image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-01-19T05:09:20.173783image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-01-19T05:09:22.845503image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-01-19T05:09:27.798223image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-01-19T05:09:30.991565image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-01-19T05:09:33.557932image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-01-19T05:09:36.098604image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-01-19T05:09:39.044924image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-01-19T05:09:05.434820image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-01-19T05:09:08.242417image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-01-19T05:09:11.948426image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-01-19T05:09:15.329561image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-01-19T05:09:17.948899image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-01-19T05:09:20.417018image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-01-19T05:09:23.097560image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-01-19T05:09:28.156893image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-01-19T05:09:31.208426image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-01-19T05:09:33.769558image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-01-19T05:09:36.316200image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-01-19T05:09:39.365186image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-01-19T05:09:05.619377image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-01-19T05:09:08.562658image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-01-19T05:09:12.231191image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-01-19T05:09:15.532338image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-01-19T05:09:18.130291image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-01-19T05:09:20.618300image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-01-19T05:09:23.356469image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-01-19T05:09:28.467788image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-01-19T05:09:31.431370image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-01-19T05:09:33.975155image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-01-19T05:09:36.509672image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-01-19T05:09:39.652629image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-01-19T05:09:05.866688image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-01-19T05:09:08.876375image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-01-19T05:09:12.557305image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-01-19T05:09:15.760229image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-01-19T05:09:18.337924image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-01-19T05:09:20.872297image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-01-19T05:09:23.760806image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-01-19T05:09:28.689482image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-01-19T05:09:31.638397image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-01-19T05:09:34.177654image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-01-19T05:09:36.726276image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-01-19T05:09:39.967717image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-01-19T05:09:06.093453image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-01-19T05:09:09.195611image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-01-19T05:09:12.929008image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-01-19T05:09:15.980114image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-01-19T05:09:18.544403image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-01-19T05:09:21.089366image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-01-19T05:09:25.017158image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-01-19T05:09:28.892999image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-01-19T05:09:31.850398image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-01-19T05:09:34.424029image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-01-19T05:09:36.926379image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-01-19T05:09:40.236569image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-01-19T05:09:06.298676image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-01-19T05:09:09.520353image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-01-19T05:09:13.182926image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-01-19T05:09:16.181509image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-01-19T05:09:18.744601image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-01-19T05:09:21.298047image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-01-19T05:09:25.410390image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-01-19T05:09:29.092145image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-01-19T05:09:32.038316image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-01-19T05:09:34.646354image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-01-19T05:09:37.126146image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-01-19T05:09:40.565851image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-01-19T05:09:06.566182image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-01-19T05:09:09.826018image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-01-19T05:09:13.422449image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-01-19T05:09:16.416211image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-01-19T05:09:18.975811image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-01-19T05:09:21.529528image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-01-19T05:09:25.766200image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-01-19T05:09:29.330175image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-01-19T05:09:32.273536image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-01-19T05:09:34.872690image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-01-19T05:09:37.339470image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-01-19T05:09:40.861876image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-01-19T05:09:06.762468image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-01-19T05:09:10.121649image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-01-19T05:09:13.647220image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-01-19T05:09:16.648842image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-01-19T05:09:19.170033image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-01-19T05:09:21.748921image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-01-19T05:09:26.112418image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-01-19T05:09:29.539647image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-01-19T05:09:32.536853image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-01-19T05:09:35.068925image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-01-19T05:09:37.552351image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-01-19T05:09:41.176064image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-01-19T05:09:06.954125image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-01-19T05:09:10.458334image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-01-19T05:09:13.824456image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-01-19T05:09:16.853340image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-01-19T05:09:19.379026image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-01-19T05:09:21.964009image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-01-19T05:09:26.450070image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-01-19T05:09:30.186189image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-01-19T05:09:32.712817image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-01-19T05:09:35.263382image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-01-19T05:09:37.749191image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-01-19T05:09:41.485676image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-01-19T05:09:07.162145image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-01-19T05:09:10.708972image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-01-19T05:09:14.478405image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-01-19T05:09:17.063103image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-01-19T05:09:19.578981image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-01-19T05:09:22.177507image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-01-19T05:09:26.787670image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-01-19T05:09:30.389376image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-01-19T05:09:32.906625image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-01-19T05:09:35.492763image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-01-19T05:09:37.949838image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-01-19T05:09:41.819075image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-01-19T05:09:07.407535image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-01-19T05:09:11.003554image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-01-19T05:09:14.689339image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-01-19T05:09:17.292875image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-01-19T05:09:19.794578image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-01-19T05:09:22.417300image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-01-19T05:09:27.110493image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-01-19T05:09:30.592330image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-01-19T05:09:33.111995image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-01-19T05:09:35.702370image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-01-19T05:09:38.193021image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-01-19T05:09:42.130961image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-01-19T05:09:07.591671image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-01-19T05:09:11.303139image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-01-19T05:09:14.890193image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-01-19T05:09:17.519110image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-01-19T05:09:19.982143image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-01-19T05:09:22.624635image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-01-19T05:09:27.453292image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-01-19T05:09:30.783250image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-01-19T05:09:33.303642image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-01-19T05:09:35.890831image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-01-19T05:09:38.410870image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Correlations

2025-01-19T05:09:51.090824image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
DayDiffuseFlowsGeneralDiffuseFlowsHourHumidityMinuteMonthPowerConsumption_Zone1PowerConsumption_Zone2PowerConsumption_Zone3TemperatureWindSpeed
Day1.000-0.0160.0060.000-0.0470.0000.0040.0260.052-0.0200.0130.175
DiffuseFlows-0.0161.0000.7860.142-0.253-0.001-0.0990.1250.097-0.0210.2610.022
GeneralDiffuseFlows0.0060.7861.0000.195-0.4590.0000.0190.2610.2420.0850.4720.157
Hour0.0000.1420.1951.000-0.2530.0000.0000.7320.6790.4670.1840.006
Humidity-0.047-0.253-0.459-0.2531.0000.001-0.011-0.300-0.309-0.212-0.378-0.181
Minute0.000-0.0010.0000.0000.0011.0000.0000.000-0.000-0.000-0.0000.007
Month0.004-0.0990.0190.000-0.0110.0001.000-0.0090.300-0.3220.3040.146
PowerConsumption_Zone10.0260.1250.2610.732-0.3000.000-0.0091.0000.8510.7480.4330.107
PowerConsumption_Zone20.0520.0970.2420.679-0.309-0.0000.3000.8511.0000.5380.3790.087
PowerConsumption_Zone3-0.020-0.0210.0850.467-0.212-0.000-0.3220.7480.5381.0000.4360.079
Temperature0.0130.2610.4720.184-0.378-0.0000.3040.4330.3790.4361.0000.326
WindSpeed0.1750.0220.1570.006-0.1810.0070.1460.1070.0870.0790.3261.000

Missing values

2025-01-19T05:09:42.517871image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
A simple visualization of nullity by column.
2025-01-19T05:09:43.273173image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.

Sample

TemperatureHumidityWindSpeedGeneralDiffuseFlowsDiffuseFlowsPowerConsumption_Zone1PowerConsumption_Zone2PowerConsumption_Zone3YearMonthDayHourMinute
Datetime
2017-01-01 00:00:006.55973.80.0830.0510.11934055.6962016128.8753820240.9638620171100
2017-01-01 00:10:006.41474.50.0830.0700.08529814.6835419375.0759920131.08434201711010
2017-01-01 00:20:006.31374.50.0800.0620.10029128.1012719006.6869319668.43373201711020
2017-01-01 00:30:006.12175.00.0830.0910.09628228.8607618361.0942218899.27711201711030
2017-01-01 00:40:005.92175.70.0810.0480.08527335.6962017872.3404318442.40964201711040
2017-01-01 00:50:005.85376.90.0810.0590.10826624.8101317416.4133718130.12048201711050
2017-01-01 01:00:005.64177.70.0800.0480.09625998.9873416993.3130717945.0602420171110
2017-01-01 01:10:005.49678.20.0850.0550.09325446.0759516661.3981817459.27711201711110
2017-01-01 01:20:005.67878.10.0810.0660.14124777.7215216227.3556217025.54217201711120
2017-01-01 01:30:005.49177.30.0820.0620.11124279.4936715939.2097316794.21687201711130
TemperatureHumidityWindSpeedGeneralDiffuseFlowsDiffuseFlowsPowerConsumption_Zone1PowerConsumption_Zone2PowerConsumption_Zone3YearMonthDayHourMinute
Datetime
2017-12-30 22:20:007.65070.10.0810.0620.12234323.9543728676.2810715684.99400201712302220
2017-12-30 22:30:007.48071.00.0850.0620.10433776.4258628230.7456315546.69868201712302230
2017-12-30 22:40:007.39071.20.0790.0660.10033387.0722427814.6670815396.87875201712302240
2017-12-30 22:50:007.34071.00.0840.0370.11932815.2091327564.2835215172.14886201712302250
2017-12-30 23:00:007.07072.50.0800.0590.09332158.1749027273.3967514987.7551020171230230
2017-12-30 23:10:007.01072.40.0800.0400.09631160.4562726857.3182014780.31212201712302310
2017-12-30 23:20:006.94772.60.0820.0510.09330430.4182526124.5780914428.81152201712302320
2017-12-30 23:30:006.90072.80.0860.0840.07429590.8745225277.6925413806.48259201712302330
2017-12-30 23:40:006.75873.00.0800.0660.08928958.1749024692.2368813512.60504201712302340
2017-12-30 23:50:006.58074.10.0810.0620.11128349.8098924055.2316713345.49820201712302350